There is a wealth of information in the database of some 150 protein structures that have been determined by the x-ray crystallography and NMR. One of the best ways to "mine" useful information from such a database is to search for patterns using the artificial neural network (NN) programming technique. In the past year, Dr. J.R. Kim wrote a flexible NN programming framework that he could adapt to apply to many different pattern recognition problems. The first problem that he tackled with this approach was the problem of predicting the secondary structure of protein molecules. By using two sets of Nns in two stages, Dr. Kim was able to (1) classify protein structures with over 70% accuracy and (2) to use this information to predict the secondary structures with 74%, 63% accuracy for the alpha- rich, beta-rich and the mixed class proteins, respectively. These numbers indicate that Dr. Kim's program performs better than any that have been reported for this purpose. On the alpha-class proteins, in particular, the improvement over the best reported technique is nearly 10%. Dr Kim is now working on predicting the correct disulfide pairing using the same programming framework.